Domain adaptation has developed for many years and most of those methods exploit rough matching between two domains. [1]} proposed a method, named Transfer Component Analysis (TCA), which learns a kernel in the reproducing kernel Hilbert space (RKHS) to minimize the maximum mean discrepancy (MMD) between domains. [2]} proposed stratified transfer learning (STL) and achieved the goal of intra-class transfer. Joint
distribution adaptation (JDA) [3]} is based on minimizing joint distribution between domains. [4]}, [5]} extended it and proposed Balanced Distribution Adaptation (BDA) which adaptively adjusts the importance of marginal distribution and conditional distribution. [6]}
proposed a method, named Local Domain Adaptation (LDA), which takes a compromise between domain- and class-level matching and utilizes high-level abstract clusters to organize data.
